Commit 51b1d6a0 authored by charlie's avatar charlie
Browse files

Merge branch 'develop' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_ref_multibroadcast

parents d2fa123c f1ecad75
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/gpu/quant_convolution.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/generate.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape miopen_quant_convolution::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(4).standard();
return op.normalize_compute_shape({inputs.at(0), inputs.at(1)});
}
argument miopen_quant_convolution::compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args) const
{
auto x_desc = make_tensor(args[0].get_shape(), int8_x4_format);
auto w_desc = make_tensor(args[1].get_shape(), int8_x4_format);
auto y_desc = make_tensor(output_shape);
float alpha = 1;
float beta = 0;
auto status = miopenConvolutionForward(ctx.get_stream().get_miopen(),
&alpha,
x_desc.get(),
args[0].implicit(),
w_desc.get(),
args[1].implicit(),
cd.get(),
algo,
&beta,
y_desc.get(),
args[3].implicit(),
args[2].implicit(),
args[2].get_shape().bytes());
if(status != miopenStatusSuccess)
{
MIGRAPHX_THROW("QUANT_CONVOLUTION: run convolution forward failed");
}
return args[3];
}
shape miopen_quant_convolution::find(context& ctx,
const shape& output_shape,
std::vector<shape> inputs)
{
shape workspace_shape{};
auto x_desc = make_tensor(inputs[0], int8_x4_format);
auto w_desc = make_tensor(inputs[1], int8_x4_format);
auto y_desc = make_tensor(output_shape);
std::size_t workspace_size = 0;
miopenConvolutionForwardGetWorkSpaceSize(ctx.get_stream().get_miopen(),
w_desc.get(),
x_desc.get(),
cd.get(),
y_desc.get(),
&workspace_size);
workspace_shape = shape{shape::int8_type, {workspace_size}};
auto x_shape = inputs[0];
auto w_shape = inputs[1];
if(int8_x4_format)
{
x_shape = pack_int8_shape(x_shape);
w_shape = pack_int8_shape(w_shape);
}
auto x = to_gpu(generate_argument(x_shape));
auto w = to_gpu(generate_argument(w_shape));
auto y = allocate_gpu(output_shape);
auto workspace = allocate_gpu(workspace_shape);
int algo_count = 1;
miopenConvAlgoPerf_t perf;
auto status = miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
x_desc.get(),
x.implicit(),
w_desc.get(),
w.implicit(),
cd.get(),
y_desc.get(),
y.implicit(),
1,
&algo_count,
&perf,
workspace.implicit(),
workspace_size,
false);
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen Quant Convolution: find convolution failed");
algo = perf.fwd_algo;
size_t solution_count;
status = miopenConvolutionForwardGetSolutionCount(ctx.get_stream().get_miopen(),
w_desc.get(),
x_desc.get(),
cd.get(),
y_desc.get(),
&solution_count);
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen Quant Convolution: get solution count failed");
std::vector<miopenConvSolution_t> solutions(solution_count);
status = miopenConvolutionForwardGetSolution(ctx.get_stream().get_miopen(),
w_desc.get(),
x_desc.get(),
cd.get(),
y_desc.get(),
solution_count,
&solution_count,
solutions.data());
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen Quant Convolution: get solution failed");
solution_id = solutions.front().solution_id;
return shape{shape::int8_type, {perf.memory}};
}
void miopen_quant_convolution::finalize(context& ctx,
const shape& output_shape,
std::vector<shape> inputs)
{
if(cd == nullptr)
cd = make_conv(op);
if(solution_id == 0)
{
// Check that workspace hasn't changed
auto size = inputs.at(2).bytes();
auto ws = find(ctx, output_shape, inputs);
if(ws.bytes() > size)
MIGRAPHX_THROW("MIOpen Quant Convolution: workspace has changed during finalization.");
}
auto x_desc = make_tensor(inputs[0], int8_x4_format);
auto w_desc = make_tensor(inputs[1], int8_x4_format);
auto y_desc = make_tensor(output_shape);
auto status = miopenConvolutionForwardCompileSolution(ctx.get_stream().get_miopen(),
w_desc.get(),
x_desc.get(),
cd.get(),
y_desc.get(),
solution_id);
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen Quant Convolution: compile solution failed");
}
shape miopen_quant_convolution::pack_int8_shape(const shape& s) const
{
if(s.type() != shape::int8_type)
{
MIGRAPHX_THROW("PACK_INT8_SHAPE: only process int8_type");
}
auto lens = s.lens();
auto strides = s.strides();
lens[1] = (lens[1] + 3) / 4 * 4;
strides[0] = strides[1] * lens[1];
return {s.type(), lens, strides};
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
......@@ -84,7 +84,7 @@ migraphx::program create_program_from_mlir(const migraphx::module& mmlir)
inputs.push_back(mm->add_parameter("output", mmlir.get_output_shapes().front()));
migraphx::gpu::context ctx;
migraphx::gpu::insert_mlir(*mm, mm->end(), compile_mlir(ctx, mmlir), inputs);
migraphx::gpu::insert_mlir(*mm, mm->end(), compile_mlir(ctx, mmlir, inputs), inputs);
return p;
}
......
......@@ -21,47 +21,27 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_DECONVOLUTION_HPP
#define MIGRAPHX_GUARD_RTGLIB_DECONVOLUTION_HPP
#include <migraphx/shape.hpp>
#include <migraphx/op/deconvolution.hpp>
#include <migraphx/gpu/miopen.hpp>
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct context;
struct miopen_deconvolution
struct quant_conv_1d : verify_program<quant_conv_1d>
{
op::deconvolution op;
shared<convolution_descriptor> cd;
miopenConvFwdAlgorithm_t algo{};
uint64_t solution_id = 0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack_join(op::deconvolution::reflect(self.op, f),
pack(f(self.solution_id, "solution_id")));
}
std::string name() const { return "gpu::deconv"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
shape find(context& ctx, const shape& output_shape, std::vector<shape> inputs);
void finalize(context& ctx, const shape& output_shape, std::vector<shape> inputs);
std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
migraphx::program create_program() const
{
return shapes.size() - 1;
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape a_shape{migraphx::shape::int8_type, {2, 3, 4}};
auto pa = mm->add_parameter("a", a_shape);
migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3}};
auto pc = mm->add_parameter("c", c_shape);
mm->add_instruction(
migraphx::make_op("quant_convolution",
{{"padding", {0}}, {"stride", {1}}, {"dilation", {1}}}),
pa,
pc);
return p;
}
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
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